1. Technical Field
The described embodiments pertain to making recommendations to a user based on location data and other data collected from mobile devices.
2. Description of Related Art
A variety of personalized recommendation systems currently exist. These systems typically record user events (such as purchases of particular products) and/or preferences (such as ratings of products and media) to generate a model for the user. This model is then used to recommend “similar” items to the user. However, there are a number of obstacles to building useful applications on top of the kinds of user models currently available.
First, existing models are not very effective until the system has observed a great deal of user behavior or preferences. Second, the interests and tastes of a typical user are more nuanced than the general genres and/or category preferences that existing models generate. Third, existing models recommend things similar to what the user has seen before, whereas many users are interested in being presented with new options to explore.
As a result, many recommendation systems are limited in their usefulness to particular scenarios. For example, they may help a pizza fan find more pizza restaurants, a science fiction fan find more science fiction novels, or a lover of romantic comedies starring Jennifer Aniston find more romantic comedies starring Jennifer Aniston. However, they cannot help the pizza fan find a great burger joint, the science fiction fan find her friend's favorite science fiction novel, or the Jennifer Aniston fan find a bar that makes a great Martini.